Competitors.
We compare methods from three lines.
We first consider the source-only (\(\mathcal {S}_{\text{only}}\) ) and supervised-target (\(\mathcal {T}_{\text{sup}}\) ) which uses only labeled source data and only labeled target data, respectively.
These two baselines serve as the lower and upper bounds for our tasks.
Secondly, we consider five popular image-based UDA methods by simply ignoring temporal information, namely DANN [1]}, JAN [2]}, ADDA [3]}, AdaBN [4]}, and MCD [5]}.
Lastly and most importantly, we compare several recent SoTA video-based UDA methods, including TA\(^3\) N [6]}, SAVA [7]}, TCoN [8]}, ABG [9]}, CoMix [10]}, CO\(^2\) A [11]}, and MA\(^2\) L-TD [12]}.
All these methods use the single modality RGB features.
We directly quote numbers reported in published papers whenever possible.
There exist recent works conducting video-based UDA using multi-modal data, e.g., RGB + Flow.
Although TranSVAE solely uses RGB features, we still take this set of methods into account.
Specifically, we consider MM-SADA [13]}, STCDA [14]}, CMCD [15]}, and CIA [16]}.
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